Steps

0. Install and load libraries

1. Read in the data

There are 4 datasets to read in, Starbucks locations, Starbucks nutrition, US population by state, and US state abbreviations. All of them are on the course GitHub.

sb_locs <- read_csv("https://raw.githubusercontent.com/JSC370/JSC370-2025/refs/heads/main/data/starbucks/starbucks-locations.csv")

## Rows: 25600 Columns: 12
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (10): Store Number, Store Name, Ownership Type, Street Address, City, St...
## dbl  (2): Longitude, Latitude
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

sb_nutr <- read_csv("https://raw.githubusercontent.com/JSC370/JSC370-2025/refs/heads/main/data/starbucks/starbucks-menu-nutrition.csv")

## Rows: 205 Columns: 7
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (2): Item, Category
## dbl (5): Calories, Fat (g), Carb. (g), Fiber (g), Protein (g)
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

usa_pop <- read_csv("https://raw.githubusercontent.com/JSC370/JSC370-2025/refs/heads/main/data/starbucks/us_state_pop.csv")

## Rows: 55 Columns: 2
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): state
## dbl (1): population
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

usa_states<-read_csv("https://raw.githubusercontent.com/JSC370/JSC370-2025/refs/heads/main/data/starbucks/states.csv")

## Rows: 51 Columns: 2
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (2): State, Abbreviation
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

2. Look at the data

Inspect each dataset to look at variable names and ensure it was imported correctly.

head(sb_locs)

## # A tibble: 6 × 12
##   `Store Number` `Store Name`            `Ownership Type` `Street Address` City 
##   <chr>          <chr>                   <chr>            <chr>            <chr>
## 1 47370-257954   Meritxell, 96           Licensed         Av. Meritxell, … Ando…
## 2 22331-212325   Ajman Drive Thru        Licensed         1 Street 69, Al… Ajman
## 3 47089-256771   Dana Mall               Licensed         Sheikh Khalifa … Ajman
## 4 22126-218024   Twofour 54              Licensed         Al Salam Street  Abu …
## 5 17127-178586   Al Ain Tower            Licensed         Khaldiya Area, … Abu …
## 6 17688-182164   Dalma Mall, Ground Flo… Licensed         Dalma Mall, Mus… Abu …
## # ℹ 7 more variables: `State/Province` <chr>, Country <chr>, Postcode <chr>,
## #   `Phone Number` <chr>, Timezone <chr>, Longitude <dbl>, Latitude <dbl>

head(sb_nutr)

## # A tibble: 6 × 7
##   Item         Category Calories `Fat (g)` `Carb. (g)` `Fiber (g)` `Protein (g)`
##   <chr>        <chr>       <dbl>     <dbl>       <dbl>       <dbl>         <dbl>
## 1 Chonga Bagel Food          300         5          50           3            12
## 2 8-Grain Roll Food          380         6          70           7            10
## 3 Almond Croi… Food          410        22          45           3            10
## 4 Apple Fritt… Food          460        23          56           2             7
## 5 Banana Nut … Food          420        22          52           2             6
## 6 Blueberry M… Food          380        16          53           1             6

head(usa_pop)

## # A tibble: 6 × 2
##   state      population
##   <chr>           <dbl>
## 1 Alabama       4779736
## 2 Alaska         710231
## 3 Arizona       6392017
## 4 Arkansas      2915918
## 5 California   37253956
## 6 Colorado      5029196

head(usa_states)

## # A tibble: 6 × 2
##   State      Abbreviation
##   <chr>      <chr>       
## 1 Alabama    AL          
## 2 Alaska     AK          
## 3 Arizona    AZ          
## 4 Arkansas   AR          
## 5 California CA          
## 6 Colorado   CO

3. Format and merge the data

Subset Starbucks data to the US.
Create counts of Starbucks stores by state.
Merge population in with the store count by state.
Inspect the range values for each variable.

sb_usa <- sb_locs |> filter(Country == "US")

sb_locs_state <- sb_usa |>
  rename(state = 'State/Province') |>
  group_by(state) |>
  summarize(n_stores = n())

# need state abbreviations
usa_pop_abbr <- 
  full_join(usa_pop, usa_states,
            by = join_by(state == State) 
            ) 
  
sb_locs_state <- full_join(sb_locs_state, usa_pop_abbr,
                           by = join_by(state == Abbreviation)
                           )

4. Use `ggplotly` for EDA

Answer the following questions:

Are the number of Starbucks proportional to the population of a state? (scatterplot)
Is the caloric distribution of Starbucks menu items different for drinks and food? (histogram)
What are the top 20 words in Starbucks menu items? (bar plot)
4a) Answer:

p1 <- ggplot(sb_locs_state, aes(x = population, y = n_stores, colour = state)) +
  geom_point(alpha = 0.8) +
  theme_bw()

ggplotly(p1)

The number of Starbucks stores generally increases with a state’s population, but it’s not perfectly proportional. Some states, like California, have way more stores than expected, likely due to factors like urbanization and demand.

4b) Answer:

p2 <- ggplot(sb_nutr, aes(x=Calories, fill=Category)) + 
  geom_histogram(alpha= 0.5) + 
  theme_bw()

ggplotly(p2)

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

The caloric distribution of Starbucks menu items is different for drinks and food. Drinks tend to have lower calorie counts and peak around 150 calories, while food items generally have higher calorie counts and peak around 450 calories.

4c) Answer:

p3 <- sb_nutr |>
  unnest_tokens(word, Item, token = "words") |>
  count(word, sort = T) |>
  head(20) |>
  ggplot(aes(fct_reorder(word, n), n)) +
  geom_col() +
  coord_flip() +
  theme_bw()

ggplotly(p3)

The top 20 most common words in Starbucks menu items include “iced,” “tazo,” “bottled,” “sandwich,” “chocolate,”coffee”, and “tea”, with “iced” appearing the most frequently. This suggests a strong emphasis on cold drinks, coffees, cholocate, and popular food items like sandwiches and desserts.

5. Scatterplots using `plot_ly()`

Create a scatterplot using plot_ly() representing the relationship between calories and carbs. Color the points by category (food or beverage). Is there a relationship, and do food or beverages tend to have more calories?

sb_nutr |>
  plot_ly(x = ~Calories, y = ~`Carb. (g)`,
          type = 'scatter', mode = 'markers', color = ~Category)

## Warning in RColorBrewer::brewer.pal(N, "Set2"): minimal value for n is 3, returning requested palette with 3 different levels
## Warning in RColorBrewer::brewer.pal(N, "Set2"): minimal value for n is 3, returning requested palette with 3 different levels

5a) Answer: There is a positive relationship between calories and carbs—items with more calories tend to have more carbs. Food items generally have higher calories than beverages, while drinks cluster more at lower calorie values.
Repeat this scatterplot but for the items that include the top 10 words. Color again by category, and add hoverinfo specifying the word in the item name. Add layout information to title the chart and the axes, and enable hovermode = "compare".
What are the top 10 words and is the plot much different than above?

topwords <- sb_nutr |>
  unnest_tokens(word, Item, token = "words") |>
  group_by(word) |>
  summarise(word_frequency = n()) |>
  arrange(across(word_frequency, desc)) |>
  head(10)

sb_nutr |>
  unnest_tokens(word, Item, token="words") |>
  filter(word %in% topwords$word) |>
  plot_ly(x = ~Calories, y = ~`Carb. (g)`,
          type = 'scatter', mode = 'markers',
          color = ~Category,
          hoverinfo = 'text',
          text = ~paste0("Item: ", word)
  ) |>
  layout(
    title = 'Cal vs Carbs', 
    xaxis = list(title = 'Calories'),
    yaxis = list(title = 'Carbs'),
    hovermode = 'compare'
  )

## Warning in RColorBrewer::brewer.pal(N, "Set2"): minimal value for n is 3, returning requested palette with 3 different levels
## Warning in RColorBrewer::brewer.pal(N, "Set2"): minimal value for n is 3, returning requested palette with 3 different levels

5b) Answer: The filtered scatterplot should look similar but will have fewer points, only showing items with the top 10 words. Since many common menu items contain these words (especially drinks like “Iced Coffee” or “Tazo Tea”), the trend between calories and carbs will still be visible, but it may have a stronger emphasis on popular drink and food categories.

6. `plot_ly` Boxplots

Create a boxplot of all of the nutritional variables in groups by the 10 item words.
Which top word has the most calories? Which top word has the most protein?

sb_nutr_long <- sb_nutr |>
  unnest_tokens(word, Item, token="words") |>
  filter(word %in% topwords$word) |>
  pivot_longer(
    cols = c(Calories, `Fat (g)`, `Carb. (g)`, `Fiber (g)`, `Protein (g)`),
    names_to = "Nutrient", values_to = "Value")

plot_ly(data = sb_nutr_long,
        x = ~word,
        y = ~Value,
        color = ~Nutrient,
        type = 'box'
  ) |>
  layout(
    title = "Nutrition values for the top 10 words items",
    xaxis = list(title = 'Item Word'),
    yaxis = list(title = 'Nutrition Value'),
    hovermode = 'compare'
  )

1. Answer: The top word with the most calories is “sandwich”, however, it has a high variability since the IQR is quite large. On the other hand, the top word with the most protein is egg, which makes sense.

7. 3D Scatterplot

Create a 3D scatterplot between Calories, Carbs, and Protein for the items containing the top 10 words
Do you see any patterns (clusters or trends)?

sb_nutr |>
  unnest_tokens(word, Item, token = "words") |>
  filter(word %in% topwords$word) |>
  plot_ly(
    x = ~Calories,
    y = ~`Carb. (g)`,
    z = ~`Protein (g)`,
    color = ~word,
    type = 'scatter3d',
    mode = 'markers',
    marker = list(size = 5)
  ) |>
  layout(
    title = "3D Scatterplot of Calories, Carbs, and Protein",
    scene = list(
      xaxis = list(title = "Calories"),
      yaxis = list(title = "Carbohydrates (g)"),
      zaxis = list(title = "Proein (g)")
    )
  )

## Warning in RColorBrewer::brewer.pal(N, "Set2"): n too large, allowed maximum for palette Set2 is 8
## Returning the palette you asked for with that many colors
## Warning in RColorBrewer::brewer.pal(N, "Set2"): n too large, allowed maximum for palette Set2 is 8
## Returning the palette you asked for with that many colors

1. Answer: The 3D scatterplot shows that items with higher calories generally have higher carbs and protein, but distinct clusters exist. Egg and sandwich items have the most protein, while chocolate, bottled, and tea items are higher in carbs but lower in protein. Drinks like black coffee and iced tea tend to be low in all three nutrients.

8. `plot_ly` Map

Create a map to visualize the number of stores per state, and another for the population by state. Add custom hover text. Use subplot to put the maps side by side.
Describe the differences if any.

# Set up mapping details
set_map_details <- list(
  scope = 'usa',
  projection = list(type = 'albers usa'),
  showlakes = TRUE,
  lakecolor = toRGB('steelblue')
)

# Make sure both maps are on the same color scale
shadeLimit <- 125

# Create hover text
sb_locs_state$hover <- with(sb_locs_state, paste("Number of Starbucks: ", n_stores, '<br>', "State: ", state.y, '<br>', "Population: ", population))

# Create the map
map1 <- plot_geo(sb_locs_state, locationmode = "USA-states") |>
  add_trace(z = ~n_stores, text = ~hover, locations = ~state,
            color = ~n_stores, colors = 'Purples') |>
  layout(title = "Starbucks store by state", geo = set_map_details)
map1

## Warning: Ignoring 4 observations

map2 <- plot_geo(sb_locs_state, locationmode = "USA-states") |>
  add_trace(z = ~population, text = ~hover, locations = ~state,
            color = ~population, colors = 'Purples') |>
  layout(title = "Population by state", geo = set_map_details)
map2

subplot(map1, map2) |>
    layout(
    annotations = list(
      list(x = 0.2, y = 1.0, text = "Starbucks Stores by State", showarrow = FALSE, xref = 'paper', yref = 'paper', font = list(size = 14, color = "black")),
      list(x = 0.8, y = 1.0, text = "Population by State", showarrow = FALSE, xref = 'paper', yref = 'paper', font = list(size = 14, color = "black"))
    )
  )

## Warning: Ignoring 4 observations

1. Answer: The left map shows the number of Starbucks stores per state, while the right map shows the population of each state. While California has the most stores and the largest population, some states (like Texas and Florida) have high populations but fewer stores than expected. This suggests that while population size influences store count, other factors like urbanization, demand, and market strategy also play a role.

Lab 11 - Interactive Visualization

Learning Goals

Lab Description

Deliverables

Steps

0. Install and load libraries

1. Read in the data

2. Look at the data

3. Format and merge the data

4. Use `ggplotly` for EDA

5. Scatterplots using `plot_ly()`

6. `plot_ly` Boxplots

7. 3D Scatterplot

8. `plot_ly` Map

Lab 11 - Interactive Visualization

Learning Goals

Lab Description

Deliverables

Steps

0. Install and load libraries

1. Read in the data

2. Look at the data

3. Format and merge the data

4. Use ggplotly for EDA

5. Scatterplots using plot_ly()

6. plot_ly Boxplots

7. 3D Scatterplot

8. plot_ly Map

4. Use `ggplotly` for EDA

5. Scatterplots using `plot_ly()`

6. `plot_ly` Boxplots

8. `plot_ly` Map